Combining GOSAT <i>X</i>CO<sub>2</sub> observations over land and ocean to improve regional CO<sub>2</sub> flux estimates
Bibliographic record
Abstract
Abstract We used the GEOS‐Chem data assimilation system to examine the impact of combining Greenhouse Gases Observing Satellite (GOSAT) X CO 2 data over land and ocean on regional CO 2 flux estimates for 2010–2012. We found that compared to assimilating only land data, combining land and ocean data produced an a posteriori CO 2 distribution that is in better agreement with independent data and fluxes that are in closer agreement with existing top‐down and bottom‐up estimates. Adding X CO 2 data over oceans changed the tropical land regions from a source of 0.64 Pg C/yr to a sink of −0.60 Pg C/yr and produced a corresponding reduction in the estimated sink in northern and southern land regions by 0.49 Pg C/yr and 0.80 Pg C/yr, respectively. This highlights the importance of improved observational coverage in the tropics to better quantify the latitudinal distribution of the terrestrial fluxes. Based only on land X CO 2 data, we estimated a strong source in northern tropical South America, which experienced wet conditions in 2010–2012. In contrast, with the land and ocean data, we estimated a sink for this wet region in the north, and a source for the seasonally dry regions in the south and east, which is consistent with our understanding of the impact of moisture availability on the carbon balance of the region. Our results suggest that using satellite data with a more zonally balanced observational coverage could help mitigate discrepancies in CO 2 flux estimates; further improvement could be expected with the greater observational coverage provided by the Orbiting Carbon Observatory‐2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".